Machine Performance Monitoring and Fault Classification using Vibration Frequency Analysis

Alghassi, Alireza; Zhou, Yu; Farbiz, Farzam

doi:10.1109/phm-besancon49106.2020.00009

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…Very often sliding systems are assessed indirectly by evaluating the quality of machining parts [3]. Direct assessment of the components of the sliding system can be performed using both off-line and on-line techniques, based on methods such as vibration analysis [4][5][6][7], laser interferometry [8,9], noise analysis [10,11], visual inspection [12,13], temperature monitoring [14,15], acoustic emission monitoring [16,17], motor current signature analysis [18,19] and thermal imaging [20][21][22].…”

Section: Condition Monitoring and Diagnostics Of Sliding Systemsmentioning

confidence: 99%

Method of Machining Centre Sliding System Fault Detection using Torque Signals and Autoencoder

Augustyn,

Fidali

2023

Acta Mechanica Et Automatica

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The sliding system of machining centres often causes maintenance and process problems. Improper operation of the sliding system can result from wear of mechanical parts and drives faults. To detect the faulty operation of the sliding system, measurements of the torque of its servomotors can be used. Servomotor controllers can measure motor current, which can be used to calculate motor torque. For research purposes, the authors used a set of torque signals from the machining centre servomotors that were acquired over a long period. The signals were collected during a diagnostic test programmed in the machining centre controller and performed once per day. In this article, a method for detecting anomalies in torque signals was presented for the condition assessment of the machining centre sliding systems. During the research, an autoencoder was used to detect the anomaly, and the condition was assessed based on the value of the reconstruction error. The results indicate that the anomaly detection method using an autoencoder is an effective solution for detecting damage to the sliding system and can be easily used in a condition monitoring system.

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Section: Condition Monitoring and Diagnostics Of Sliding Systemsmentioning

confidence: 99%

Method of Machining Centre Sliding System Fault Detection using Torque Signals and Autoencoder

Augustyn,

Fidali

2023

Acta Mechanica Et Automatica

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“…An interesting application of MT performance monitoring and fault classification was developed with a Random Forest structure in Reference [21] with the FFT, peaks and RMS of the vibration signal from the spindle. The obtained results showed a structured approach to monitor the machine health and performance in real time.…”

Section: Machine Condition Monitoringmentioning

confidence: 99%

“…Typically, there are three main domains: (i) Time Domain (TD), (ii) Frequency Domain (FD) and (iii) Time-Frequency Domain (TFD). For each domain, there are typical coefficients, for example, statistical features (e.g., kurtosis, standard deviation) are used for TD signals [20], while other types of features are obtained (e.g., RMS and peak values [21]) for FD signals.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances on Machine Learning Applications in Machining Processes

2021

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This study aims to present an overall review of the recent research status regarding Machine Learning (ML) applications in machining processes. In the current industrial systems, processes require the capacity to adapt to manufacturing conditions continuously, guaranteeing high performance- in terms of production quality and equipment availability. Artificial Intelligence (AI) offers new opportunities to develop and integrate innovative solutions in conventional machine tools to reduce undesirable effects during operational activities. In particular, the significant increase of the computational capacity may permit the application of complex algorithms to big data volumes in a short time, expanding the potentialities of ML techniques. ML applications are present in several contexts of machining processes, from roughness quality prediction to tool condition monitoring. This review focuses on recent applications and implications, classifying the main problems that may be solved using ML related to the machining quality, energy consumption and conditional monitoring. Finally, a discussion on the advantages and limits of ML algorithms is summarized for future investigations.

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Generalized Anomaly Detection Algorithm Based on Time Series Statistical Features

Alghassi

2021

Intelligent Systems Reference Library

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Machine Performance Monitoring and Fault Classification using Vibration Frequency Analysis

Cited by 7 publications

References 6 publications

Method of Machining Centre Sliding System Fault Detection using Torque Signals and Autoencoder

Method of Machining Centre Sliding System Fault Detection using Torque Signals and Autoencoder

Recent Advances on Machine Learning Applications in Machining Processes

Generalized Anomaly Detection Algorithm Based on Time Series Statistical Features

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